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Record W2809032332 · doi:10.1111/mbe.12168

Longer Fixation Times During Reading Are Correlated With Decreased Connectivity in Cognitive‐Control Brain Regions During Rest in Children

2018· article· en· W2809032332 on OpenAlexaff
Tzipi Horowitz‐Kraus, Christopher A. DiCesare, Adam W. Kiefer

Bibliographic record

VenueMind Brain and Education · 2018
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsDiscovery Centre
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsPsychologyCognitionFixation (population genetics)Reading (process)Rest (music)Cognitive psychologyDevelopmental psychologyNeuroscienceLinguisticsMedicine

Abstract

fetched live from OpenAlex

Dyslexia, or reading difficulty (RD), is characterized by slow, inaccurate reading and accompanied by deficit in executive functions (EF) and altered functional connectivity (FC) in the related networks (i.e., cingulo-opercular). Individuals with RD also present with altered oculomotor gaze patterns that include longer fixation times. The researchers examined the relationship between fixation times and FC of neural circuits related to EF during rest in children with RD and typical readers. Nineteen children participated in a 10-min resting-state scan. FC analysis was performed with the anterior cingulate cortex (ACC), related to cognitive control, chosen as a seed. Fixation time during word reading was used as a covariate of interest. Results demonstrated that FC between the ACC and the left inferior frontal cortex pars triangularis and left inferior prefrontal cortex during rest were negatively correlated with fixation times during word reading. These exploratory results support the critical role for the cingulo-opercular network, which is related to cognitive control, in the reading process, and likely also in reading impairment.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.576

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.278
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2018
Admission routes1
Has abstractyes

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